Kathleen T. Famiglietti

The introduction of a diagnostic decision support system (DXplain™) into the workflow of a teaching hospital service can decrease the cost of service for diagnostically challenging Diagnostic Related Groups (DRGs)

BACKGROUND In an era of short inpatient stays, residents may overlook relevant elements of the differential diagnosis as they try to evaluate and treat patients. However, if a residents first principal diagnosis is wrong, the patients appropriate evaluation and treatment may take longer, cost more, and lead to worse outcomes. A diagnostic decision support system may lead to the generation of a broader differential diagnosis that more often includes the correct diagnosis, permitting a shorter, more effective, and less costly hospital stay. METHODS We provided residents on General Medicine services access to DXplain, an established computer-based diagnostic decision support system, for 6 months. We compared charges and cost of service for diagnostically challenging cases seen during the fourth through sixth month of access to DXplain (intervention period) to control cases seen in the 6 months before the system was made available. RESULTS 564 cases were identified as diagnostically challenging by our criteria during the intervention period along with 1173 cases during the control period. Total charges were

Presence of key findings in the medical record prior to a documented high-risk diagnosis

1281 lower (p=.006), Medicare Part A charges

Sharing Computer-Based Simulations for Clinical Education

1032 lower (p=0.006) and cost of service

DXplain on the Internet.

990 lower (p=0.001) per admission in the intervention cases than in control cases. CONCLUSIONS Using DXplain on all diagnostically challenging cases might save our medical center over

Internal medicine resident satisfaction with a diagnostic decision support system (DXplain) introduced on a teaching hospital service.

2,000,000 a year on the General Medicine Services alone. Using clinical diagnostic decision support systems may improve quality and decrease cost substantially at teaching hospitals.

DXplain: Patterns of Use of a Mature Expert System.

BACKGROUND Failure or delay in diagnosis is a common preventable source of error. The authors sought to determine the frequency with which high-information clinical findings (HIFs) suggestive of a high-risk diagnosis (HRD) appear in the medical record before HRD documentation. METHODS A knowledge base from a diagnostic decision support system was used to identify HIFs for selected HRDs: lumbar disc disease, myocardial infarction, appendicitis, and colon, breast, lung, ovarian and bladder carcinomas. Two physicians reviewed at least 20 patient records retrieved from a research patient data registry for each of these eight HRDs and for age- and gender-compatible controls. Records were searched for HIFs in visit notes that were created before the HRD was established in the electronic record and in general medical visit notes for controls. RESULTS 25% of records reviewed (61/243) contained HIFs in notes before the HRD was established. The mean duration between HIFs first occurring in the record and time of diagnosis ranged from 19 days for breast cancer to 2 years for bladder cancer. In three of the eight HRDs, HIFs were much less likely in control patients without the HRD. CONCLUSIONS In many records of patients with an HRD, HIFs were present before the HRD was established. Reasons for delay include non-compliance with recommended follow-up, unusual presentation of a disease, and system errors (eg, lack of laboratory follow-up). The presence of HIFs in clinical records suggests a potential role for the integration of diagnostic decision support into the clinical workflow to provide reminder alerts to improve the diagnostic focus.

DXplain--demonstration and discussion of a diagnostic decision support system.

During the past decade, Massachusetts General Hospital has written and distributed a series of computer-based simulations designed to supplement other instruction in the process of clinical problem-solving. Sharing educational resources is a commonly accepted academic responsibility, and sharing the end products of federally sponsored research and development is implicit in grants and contracts. Sharing computer-based materials can be accomplished in a variety of ways. The purposes of this chapter are (1) to describe three modes of sharing (program transfer, remote authoring, and networking), (2) to consider the costs and benefits of each mode from the points of view of both the originating and receiving institutions, and (3) to describe the evolution of a community of medical educators whose goal it is to enhance sharing of educational resources among institutions. This chapter is an overview of the issues and concerns associated with sharing computer-based educational materials, and it is written from the perspective of an originating or host institution.